Overview

Dataset statistics

Number of variables44
Number of observations198
Missing cells3197
Missing cells (%)36.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.2 KiB
Average record size in memory352.6 B

Variable types

CAT21
NUM12
DATE4
UNSUPPORTED4
BOOL3

Warnings

Select project has constant value "198" Constant
FR1_ProjectName has constant value "198" Constant
FR3 Area Units has constant value "198" Constant
Asset ID: has a high cardinality: 194 distinct values High cardinality
Asset Description has a high cardinality: 195 distinct values High cardinality
Comments has a high cardinality: 79 distinct values High cardinality
Bund height (m) is highly correlated with ObjectID and 4 other fieldsHigh correlation
ObjectID is highly correlated with Bund height (m) and 1 other fieldsHigh correlation
Bund width (m) is highly correlated with Installed Cost (£) and 1 other fieldsHigh correlation
Installed Cost (£) is highly correlated with Bund width (m)High correlation
Bund length (m) is highly correlated with ObjectID and 3 other fieldsHigh correlation
Storage Created (m3) is highly correlated with Bund width (m) and 1 other fieldsHigh correlation
FR3_AreaRough is highly correlated with Storage Created (m3)High correlation
longitude is highly correlated with Bund height (m) and 1 other fieldsHigh correlation
latitude is highly correlated with Bund height (m) and 1 other fieldsHigh correlation
log10_price is highly correlated with Bund height (m)High correlation
Watercourse Type has 5 (2.5%) missing values Missing
Stream Width (m) has 42 (21.2%) missing values Missing
Land Drainage Consent Difficulty has 10 (5.1%) missing values Missing
Ecological Consent Difficulty has 13 (6.6%) missing values Missing
Average member length in Leaky Barrier (m) has 41 (20.7%) missing values Missing
Wood Diameter (cm) has 66 (33.3%) missing values Missing
Height of Leaky Barrier above bed (cm) has 44 (22.2%) missing values Missing
Height of Leaky Barrier above bank (cm) has 115 (58.1%) missing values Missing
Wood Species Used has 170 (85.9%) missing values Missing
Other Wood Species has 190 (96.0%) missing values Missing
Bund height (m) has 194 (98.0%) missing values Missing
Bund width (m) has 194 (98.0%) missing values Missing
Bund length (m) has 194 (98.0%) missing values Missing
Bund Material has 189 (95.5%) missing values Missing
Gully Block Length (m) has 198 (100.0%) missing values Missing
Gully Block Width (m) has 198 (100.0%) missing values Missing
Gully Block Material has 198 (100.0%) missing values Missing
Soil Equipment or Technique Used has 198 (100.0%) missing values Missing
Flood Efficacy has 37 (18.7%) missing values Missing
FR3_AreaRough has 190 (96.0%) missing values Missing
Storage Created (m3) has 3 (1.5%) missing values Missing
FR3_AreaIncreasedLoss has 195 (98.5%) missing values Missing
Changed Flood Pathway? has 66 (33.3%) missing values Missing
Reduced Erosion? has 155 (78.3%) missing values Missing
Asset Condition has 13 (6.6%) missing values Missing
Date Assessed has 11 (5.6%) missing values Missing
Comments has 84 (42.4%) missing values Missing
Creator has 172 (86.9%) missing values Missing
Editor has 12 (6.1%) missing values Missing
Asset ID: is uniformly distributed Uniform
Asset Description is uniformly distributed Uniform
ObjectID has unique values Unique
GlobalID has unique values Unique
CreationDate has unique values Unique
longitude has unique values Unique
latitude has unique values Unique
Gully Block Length (m) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Gully Block Width (m) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Gully Block Material is an unsupported type, check if it needs cleaning or further analysis Unsupported
Soil Equipment or Technique Used is an unsupported type, check if it needs cleaning or further analysis Unsupported
Installed Cost (£) has 3 (1.5%) zeros Zeros
Height of Leaky Barrier above bed (cm) has 3 (1.5%) zeros Zeros
Height of Leaky Barrier above bank (cm) has 42 (21.2%) zeros Zeros
FR3_AreaRough has 3 (1.5%) zeros Zeros
Storage Created (m3) has 2 (1.0%) zeros Zeros

Reproduction

Analysis started2020-10-13 23:17:27.362610
Analysis finished2020-10-13 23:18:15.679643
Duration48.32 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

ObjectID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct198
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean677.5050505
Minimum42
Maximum1254
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-14T00:18:16.114538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile289.85
Q1454.25
median650.5
Q3718.75
95-th percentile1211
Maximum1254
Range1212
Interquartile range (IQR)264.5

Descriptive statistics

Standard deviation292.2571663
Coefficient of variation (CV)0.4313726755
Kurtosis-0.4231717337
Mean677.5050505
Median Absolute Deviation (MAD)190
Skewness0.4629322464
Sum134146
Variance85414.25124
MonotocityNot monotonic
2020-10-14T00:18:16.441590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
112210.5%
 
67410.5%
 
67010.5%
 
66910.5%
 
66810.5%
 
66710.5%
 
66610.5%
 
66510.5%
 
66410.5%
 
61910.5%
 
Other values (188)18894.9%
 
ValueCountFrequency (%) 
4210.5%
 
9010.5%
 
9110.5%
 
9210.5%
 
9310.5%
 
ValueCountFrequency (%) 
125410.5%
 
125310.5%
 
125210.5%
 
125110.5%
 
125010.5%
 

GlobalID
Categorical

UNIQUE

Distinct198
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
cbf127d8-106a-4d9d-949c-473b3078ad42
 
1
6f0e9956-8d95-4ebf-a540-9f0cabc735e9
 
1
945b0df9-3996-4d09-8b0f-b0ab40ae8198
 
1
c53075f1-4e5b-4885-b250-feecdaa6493e
 
1
d29a1053-7fe8-4a50-9403-74ccbd4e45fb
 
1
Other values (193)
193 
ValueCountFrequency (%) 
cbf127d8-106a-4d9d-949c-473b3078ad4210.5%
 
6f0e9956-8d95-4ebf-a540-9f0cabc735e910.5%
 
945b0df9-3996-4d09-8b0f-b0ab40ae819810.5%
 
c53075f1-4e5b-4885-b250-feecdaa6493e10.5%
 
d29a1053-7fe8-4a50-9403-74ccbd4e45fb10.5%
 
1d32eb4f-29cb-4dd9-8e9c-4d94d643dc0210.5%
 
28d45f34-03aa-425c-a28a-ede44093cc7910.5%
 
ec128f1e-7bdb-433c-8566-70459f0584a310.5%
 
aae98524-b3a9-4337-8ff5-88ed44dc2b3110.5%
 
6bb19f39-da8c-4e6a-95d4-d202ba6611ad10.5%
 
Other values (188)18894.9%
 
2020-10-14T00:18:16.817125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique198 ?
Unique (%)100.0%
2020-10-14T00:18:17.042649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length36
Mean length36
Min length36

Select project
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
A017
198 
ValueCountFrequency (%) 
A017198100.0%
 
2020-10-14T00:18:17.235192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:17.389974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:17.566201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

FR1_ProjectName
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Shipston
198 
ValueCountFrequency (%) 
Shipston198100.0%
 
2020-10-14T00:18:17.811075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:18.055312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:18.313345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Asset ID:
Categorical

HIGH CARDINALITY
UNIFORM

Distinct194
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
A017_2002131359
 
2
A017_2005041404
 
2
A017_2003021344
 
2
A017_2004271037
 
2
A017_2005131053
 
1
Other values (189)
189 
ValueCountFrequency (%) 
A017_200213135921.0%
 
A017_200504140421.0%
 
A017_200302134421.0%
 
A017_200427103721.0%
 
A017_200513105310.5%
 
A017_191231200410.5%
 
A017_200815111510.5%
 
A017_200213141010.5%
 
A017_201007144910.5%
 
A017_200427102210.5%
 
Other values (184)18492.9%
 
2020-10-14T00:18:18.585337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique190 ?
Unique (%)96.0%
2020-10-14T00:18:18.852615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length15
Min length15

Asset Type
Categorical

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
leaky_barriers
165 
offline_storage_areas
18 
runoff_pathway_management
 
12
riparian_woodland_creation
 
1
river_restoration
 
1
ValueCountFrequency (%) 
leaky_barriers16583.3%
 
offline_storage_areas189.1%
 
runoff_pathway_management126.1%
 
riparian_woodland_creation10.5%
 
river_restoration10.5%
 
cross_slope_woodland_creation10.5%
 
2020-10-14T00:18:19.073410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)1.5%
2020-10-14T00:18:19.235002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:19.445648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length14
Mean length15.45454545
Min length14

Asset Description
Categorical

HIGH CARDINALITY
UNIFORM

Distinct195
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
DELETE
 
4
KNEEBATS08LB5
 
1
KNEECOMP04LB3
 
1
KNEEBEECH02LB1
 
1
KNEELONG51LB51
 
1
Other values (190)
190 
ValueCountFrequency (%) 
DELETE42.0%
 
KNEEBATS08LB510.5%
 
KNEECOMP04LB310.5%
 
KNEEBEECH02LB110.5%
 
KNEELONG51LB5110.5%
 
KNEELONG07LB710.5%
 
KNEELONG36LB3610.5%
 
KNEELONG37LB3710.5%
 
KNEELONG22LB2210.5%
 
Hen110.5%
 
Other values (185)18593.4%
 
2020-10-14T00:18:19.707811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique194 ?
Unique (%)98.0%
2020-10-14T00:18:19.968585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length13
Mean length12.52020202
Min length1
Distinct48
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2017-05-31 12:00:00
Maximum2020-10-07 11:00:00
2020-10-14T00:18:20.199146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:20.430127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

Installed Cost (£)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.2828283
Minimum0
Maximum6000
Zeros3
Zeros (%)1.5%
Memory size1.5 KiB
2020-10-14T00:18:20.649551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1100
median100
Q3150
95-th percentile1560
Maximum6000
Range6000
Interquartile range (IQR)50

Descriptive statistics

Standard deviation697.2500231
Coefficient of variation (CV)2.461321173
Kurtosis30.48896583
Mean283.2828283
Median Absolute Deviation (MAD)50
Skewness5.060124046
Sum56090
Variance486157.5947
MonotocityNot monotonic
2020-10-14T00:18:20.862719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
1006532.8%
 
13015.2%
 
1502311.6%
 
1202211.1%
 
60105.1%
 
23094.5%
 
16073.5%
 
26052.5%
 
20052.5%
 
031.5%
 
Other values (15)199.6%
 
ValueCountFrequency (%) 
031.5%
 
13015.2%
 
60105.1%
 
1006532.8%
 
1202211.1%
 
ValueCountFrequency (%) 
600010.5%
 
400010.5%
 
380010.5%
 
267010.5%
 
215521.0%
 

Watercourse Type
Categorical

MISSING

Distinct1
Distinct (%)0.5%
Missing5
Missing (%)2.5%
Memory size1.5 KiB
ordinary
193 
ValueCountFrequency (%) 
ordinary19397.5%
 
(Missing)52.5%
 
2020-10-14T00:18:21.104822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:21.284741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:21.393326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.873737374
Min length3

Stream Width (m)
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)7.7%
Missing42
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean1.510897436
Minimum0.5
Maximum38
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-14T00:18:21.598058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.6
Q11
median1
Q31.5
95-th percentile2
Maximum38
Range37.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.985736495
Coefficient of variation (CV)1.976134464
Kurtosis146.5455001
Mean1.510897436
Median Absolute Deviation (MAD)0.25
Skewness11.93046713
Sum235.7
Variance8.914622415
MonotocityNot monotonic
2020-10-14T00:18:21.810681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
17135.9%
 
1.53919.7%
 
22010.1%
 
0.7584.0%
 
0.563.0%
 
0.631.5%
 
331.5%
 
1.2521.0%
 
1.210.5%
 
0.710.5%
 
Other values (2)21.0%
 
(Missing)4221.2%
 
ValueCountFrequency (%) 
0.563.0%
 
0.631.5%
 
0.710.5%
 
0.7584.0%
 
17135.9%
 
ValueCountFrequency (%) 
3810.5%
 
410.5%
 
331.5%
 
22010.1%
 
1.53919.7%
 
Distinct3
Distinct (%)1.6%
Missing10
Missing (%)5.1%
Memory size1.5 KiB
easy
174 
moderate
 
11
n_a
 
3
ValueCountFrequency (%) 
easy17487.9%
 
moderate115.6%
 
n_a31.5%
 
(Missing)105.1%
 
2020-10-14T00:18:22.192115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:22.363276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:22.508448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length4.156565657
Min length3
Distinct3
Distinct (%)1.6%
Missing13
Missing (%)6.6%
Memory size1.5 KiB
n_a
173 
easy
 
11
moderate
 
1
ValueCountFrequency (%) 
n_a17387.4%
 
easy115.6%
 
moderate10.5%
 
(Missing)136.6%
 
2020-10-14T00:18:22.760679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.5%
2020-10-14T00:18:22.944443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:23.105599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length3.080808081
Min length3

Average member length in Leaky Barrier (m)
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)7.6%
Missing41
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean3.573248408
Minimum1.5
Maximum15
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-14T00:18:23.380013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2
Q12.5
median3
Q34
95-th percentile8
Maximum15
Range13.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.843052866
Coefficient of variation (CV)0.5157919786
Kurtosis10.43237514
Mean3.573248408
Median Absolute Deviation (MAD)1
Skewness2.63726579
Sum561
Variance3.396843867
MonotocityNot monotonic
2020-10-14T00:18:23.580207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
34422.2%
 
22814.1%
 
2.52311.6%
 
42211.1%
 
5115.6%
 
694.5%
 
3.584.0%
 
863.0%
 
1.521.0%
 
1021.0%
 
Other values (2)21.0%
 
(Missing)4120.7%
 
ValueCountFrequency (%) 
1.521.0%
 
22814.1%
 
2.52311.6%
 
34422.2%
 
3.584.0%
 
ValueCountFrequency (%) 
1510.5%
 
1021.0%
 
863.0%
 
694.5%
 
5115.6%
 

Wood Diameter (cm)
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)9.8%
Missing66
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean21.57575758
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-14T00:18:23.836572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median20
Q325
95-th percentile34.5
Maximum100
Range99
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.07219281
Coefficient of variation (CV)0.5131774757
Kurtosis19.258222
Mean21.57575758
Median Absolute Deviation (MAD)5
Skewness3.197978006
Sum2848
Variance122.5934536
MonotocityNot monotonic
2020-10-14T00:18:24.236639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
153417.2%
 
252713.6%
 
202613.1%
 
302010.1%
 
10147.1%
 
4031.5%
 
7.521.0%
 
6010.5%
 
110.5%
 
5010.5%
 
Other values (3)31.5%
 
(Missing)6633.3%
 
ValueCountFrequency (%) 
110.5%
 
7.521.0%
 
10147.1%
 
1210.5%
 
153417.2%
 
ValueCountFrequency (%) 
10010.5%
 
6010.5%
 
5010.5%
 
4510.5%
 
4031.5%
 

Height of Leaky Barrier above bed (cm)
Real number (ℝ≥0)

MISSING
ZEROS

Distinct18
Distinct (%)11.7%
Missing44
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean26.7012987
Minimum0
Maximum200
Zeros3
Zeros (%)1.5%
Memory size1.5 KiB
2020-10-14T00:18:24.655813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q115
median20
Q330
95-th percentile60
Maximum200
Range200
Interquartile range (IQR)15

Descriptive statistics

Standard deviation24.68967657
Coefficient of variation (CV)0.9246620115
Kurtosis19.58612368
Mean26.7012987
Median Absolute Deviation (MAD)7.5
Skewness3.681385316
Sum4112
Variance609.580129
MonotocityNot monotonic
2020-10-14T00:18:24.908448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
154924.7%
 
302110.6%
 
20157.6%
 
25136.6%
 
10126.1%
 
6094.5%
 
4573.5%
 
4073.5%
 
3542.0%
 
10031.5%
 
Other values (8)147.1%
 
(Missing)4422.2%
 
ValueCountFrequency (%) 
031.5%
 
131.5%
 
1.510.5%
 
531.5%
 
7.510.5%
 
ValueCountFrequency (%) 
20010.5%
 
15010.5%
 
10031.5%
 
6094.5%
 
5010.5%
 

Height of Leaky Barrier above bank (cm)
Real number (ℝ≥0)

MISSING
ZEROS

Distinct16
Distinct (%)19.3%
Missing115
Missing (%)58.1%
Infinite0
Infinite (%)0.0%
Mean35.01686747
Minimum0
Maximum1050
Zeros42
Zeros (%)21.2%
Memory size1.5 KiB
2020-10-14T00:18:25.159667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q330
95-th percentile100
Maximum1050
Range1050
Interquartile range (IQR)30

Descriptive statistics

Standard deviation118.7153521
Coefficient of variation (CV)3.390233357
Kurtosis67.06044716
Mean35.01686747
Median Absolute Deviation (MAD)0
Skewness7.846383132
Sum2906.4
Variance14093.33483
MonotocityNot monotonic
2020-10-14T00:18:25.363244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
04221.2%
 
1584.0%
 
10073.5%
 
5063.0%
 
2052.5%
 
3042.0%
 
4521.0%
 
11010.5%
 
2510.5%
 
510.5%
 
Other values (6)63.0%
 
(Missing)11558.1%
 
ValueCountFrequency (%) 
04221.2%
 
0.210.5%
 
1.210.5%
 
510.5%
 
1010.5%
 
ValueCountFrequency (%) 
105010.5%
 
20010.5%
 
11010.5%
 
10073.5%
 
7510.5%
 

Wood Species Used
Categorical

MISSING

Distinct4
Distinct (%)14.3%
Missing170
Missing (%)85.9%
Memory size1.5 KiB
willow_dead
18 
other
softwood
 
1
willow_live
 
1
ValueCountFrequency (%) 
willow_dead189.1%
 
other84.0%
 
softwood10.5%
 
willow_live10.5%
 
(Missing)17085.9%
 
2020-10-14T00:18:25.709557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)7.1%
2020-10-14T00:18:26.464925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:26.792987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length3.873737374
Min length3

Other Wood Species
Categorical

MISSING

Distinct2
Distinct (%)25.0%
Missing190
Missing (%)96.0%
Memory size1.5 KiB
Leylandi
Machined
ValueCountFrequency (%) 
Leylandi63.0%
 
Machined21.0%
 
(Missing)19096.0%
 
2020-10-14T00:18:27.104943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:27.324113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:27.443421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length3.202020202
Min length3

Bund height (m)
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing194
Missing (%)98.0%
Memory size1.5 KiB
2
0.6
2.5
ValueCountFrequency (%) 
221.0%
 
0.610.5%
 
2.510.5%
 
(Missing)19498.0%
 
2020-10-14T00:18:27.663936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)50.0%
2020-10-14T00:18:27.833389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:27.963663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Bund width (m)
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing194
Missing (%)98.0%
Memory size1.5 KiB
3
30
1
ValueCountFrequency (%) 
321.0%
 
3010.5%
 
110.5%
 
(Missing)19498.0%
 
2020-10-14T00:18:28.207301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)50.0%
2020-10-14T00:18:28.792212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:28.931638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.005050505
Min length3

Bund length (m)
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing194
Missing (%)98.0%
Memory size1.5 KiB
1
2
12
ValueCountFrequency (%) 
121.0%
 
210.5%
 
1210.5%
 
(Missing)19498.0%
 
2020-10-14T00:18:29.284985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)50.0%
2020-10-14T00:18:29.585469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:29.918614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.005050505
Min length3

Bund Material
Categorical

MISSING

Distinct1
Distinct (%)11.1%
Missing189
Missing (%)95.5%
Memory size1.5 KiB
clay
ValueCountFrequency (%) 
clay94.5%
 
(Missing)18995.5%
 
2020-10-14T00:18:30.190958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:30.335264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:30.454907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.045454545
Min length3

Gully Block Length (m)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing198
Missing (%)100.0%
Memory size1.7 KiB

Gully Block Width (m)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing198
Missing (%)100.0%
Memory size1.7 KiB

Gully Block Material
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing198
Missing (%)100.0%
Memory size1.7 KiB

Soil Equipment or Technique Used
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing198
Missing (%)100.0%
Memory size1.7 KiB

Flood Efficacy
Categorical

MISSING

Distinct3
Distinct (%)1.9%
Missing37
Missing (%)18.7%
Memory size1.5 KiB
moderate
134 
high
15 
low
 
12
ValueCountFrequency (%) 
moderate13467.7%
 
high157.6%
 
low126.1%
 
(Missing)3718.7%
 
2020-10-14T00:18:30.662841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:30.817375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:30.933516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length6.45959596
Min length3

FR3 Area Units
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
ha
198 
ValueCountFrequency (%) 
ha198100.0%
 
2020-10-14T00:18:31.136394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:31.375312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:31.569693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

FR3_AreaRough
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct5
Distinct (%)62.5%
Missing190
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean0.5875
Minimum0
Maximum2
Zeros3
Zeros (%)1.5%
Memory size1.5 KiB
2020-10-14T00:18:32.026675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q31.125
95-th percentile1.825
Maximum2
Range2
Interquartile range (IQR)1.125

Descriptive statistics

Standard deviation0.8025628591
Coefficient of variation (CV)1.366064441
Kurtosis-0.6247378549
Mean0.5875
Median Absolute Deviation (MAD)0.1
Skewness1.034701614
Sum4.7
Variance0.6441071429
MonotocityNot monotonic
2020-10-14T00:18:32.432495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
031.5%
 
0.121.0%
 
210.5%
 
1.510.5%
 
110.5%
 
(Missing)19096.0%
 
ValueCountFrequency (%) 
031.5%
 
0.121.0%
 
110.5%
 
1.510.5%
 
210.5%
 
ValueCountFrequency (%) 
210.5%
 
1.510.5%
 
110.5%
 
0.121.0%
 
031.5%
 

Storage Created (m3)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct39
Distinct (%)20.0%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean145.7128205
Minimum0
Maximum4154
Zeros2
Zeros (%)1.0%
Memory size1.5 KiB
2020-10-14T00:18:32.886517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.5
Q125
median40
Q380
95-th percentile633
Maximum4154
Range4154
Interquartile range (IQR)55

Descriptive statistics

Standard deviation413.5702356
Coefficient of variation (CV)2.838255647
Kurtosis51.81934142
Mean145.7128205
Median Absolute Deviation (MAD)20
Skewness6.491987871
Sum28414
Variance171040.3398
MonotocityNot monotonic
2020-10-14T00:18:33.327905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
252010.1%
 
152010.1%
 
50199.6%
 
40189.1%
 
30178.6%
 
60136.6%
 
100115.6%
 
20115.6%
 
1084.0%
 
8073.5%
 
Other values (29)5125.8%
 
ValueCountFrequency (%) 
021.0%
 
1084.0%
 
152010.1%
 
20115.6%
 
252010.1%
 
ValueCountFrequency (%) 
415410.5%
 
234410.5%
 
210010.5%
 
134510.5%
 
123810.5%
 

FR3_AreaIncreasedLoss
Boolean

MISSING

Distinct1
Distinct (%)33.3%
Missing195
Missing (%)98.5%
Memory size1.5 KiB
0
 
3
(Missing)
195 
ValueCountFrequency (%) 
031.5%
 
(Missing)19598.5%
 
2020-10-14T00:18:34.201086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)1.5%
Missing66
Missing (%)33.3%
Memory size1.5 KiB
no
117 
yes
15 
(Missing)
66 
ValueCountFrequency (%) 
no11759.1%
 
yes157.6%
 
(Missing)6633.3%
 
2020-10-14T00:18:34.455033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Reduced Erosion?
Boolean

MISSING

Distinct2
Distinct (%)4.7%
Missing155
Missing (%)78.3%
Memory size1.5 KiB
no
22 
yes
21 
(Missing)
155 
ValueCountFrequency (%) 
no2211.1%
 
yes2110.6%
 
(Missing)15578.3%
 
2020-10-14T00:18:34.676907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Asset Condition
Categorical

MISSING

Distinct4
Distinct (%)2.2%
Missing13
Missing (%)6.6%
Memory size1.5 KiB
good
156 
moderate
25 
poor
 
3
absent
 
1
ValueCountFrequency (%) 
good15678.8%
 
moderate2512.6%
 
poor31.5%
 
absent10.5%
 
(Missing)136.6%
 
2020-10-14T00:18:35.065471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.5%
2020-10-14T00:18:35.432287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:35.851297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length4.449494949
Min length3

Date Assessed
Date

MISSING

Distinct45
Distinct (%)24.1%
Missing11
Missing (%)5.6%
Memory size1.5 KiB
Minimum2018-05-31 12:00:00
Maximum2020-10-07 11:00:00
2020-10-14T00:18:36.116516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:36.388905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)

Comments
Categorical

HIGH CARDINALITY
MISSING

Distinct79
Distinct (%)69.3%
Missing84
Missing (%)42.4%
Memory size1.5 KiB
Slatted
25 
Clear brash
 
6
Slight scouring
 
2
Badly silted. Low priority
 
2
Clear Brash
 
2
Other values (74)
77 
ValueCountFrequency (%) 
Slatted2512.6%
 
Clear brash63.0%
 
Slight scouring21.0%
 
Badly silted. Low priority21.0%
 
Clear Brash21.0%
 
Brash build up above dam21.0%
 
Good size21.0%
 
Good pathway in21.0%
 
Maintenance review due Q1 202010.5%
 
slight scouring10.5%
 
Other values (69)6934.8%
 
(Missing)8442.4%
 
2020-10-14T00:18:36.749112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique71 ?
Unique (%)62.3%
2020-10-14T00:18:37.157423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length60
Median length7
Mean length12.58585859
Min length3

CreationDate
Date

UNIQUE

Distinct198
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2019-08-03 18:37:53
Maximum2020-10-07 15:13:58
2020-10-14T00:18:37.487731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:37.769018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Creator
Categorical

MISSING

Distinct1
Distinct (%)3.8%
Missing172
Missing (%)86.9%
Memory size1.5 KiB
gsmithadmin
26 
ValueCountFrequency (%) 
gsmithadmin2613.1%
 
(Missing)17286.9%
 
2020-10-14T00:18:38.047702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:38.187584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:38.321862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length4.050505051
Min length3
Distinct197
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2020-01-29 14:54:16
Maximum2020-10-07 15:13:58
2020-10-14T00:18:38.548651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:38.814205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Editor
Categorical

MISSING

Distinct1
Distinct (%)0.5%
Missing12
Missing (%)6.1%
Memory size1.5 KiB
gsmithadmin
186 
ValueCountFrequency (%) 
gsmithadmin18693.9%
 
(Missing)126.1%
 
2020-10-14T00:18:39.089243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T00:18:39.236392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:39.485774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.51515152
Min length3

longitude
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct198
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.709514809
Minimum-1.81663
Maximum-1.5606919
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-14T00:18:40.339280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.81663
5-th percentile-1.777469068
Q1-1.757926072
median-1.728301006
Q3-1.661295013
95-th percentile-1.571073804
Maximum-1.5606919
Range0.2559381
Interquartile range (IQR)0.09663105881

Descriptive statistics

Standard deviation0.06118858184
Coefficient of variation (CV)-0.03579295219
Kurtosis-0.03131510903
Mean-1.709514809
Median Absolute Deviation (MAD)0.0369472
Skewness0.7978542129
Sum-338.4839322
Variance0.003744042547
MonotocityNot monotonic
2020-10-14T00:18:41.049342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1.75806455910.5%
 
-1.77085333310.5%
 
-1.77497970310.5%
 
-1.65678572910.5%
 
-1.75848944310.5%
 
-1.77784382310.5%
 
-1.561509910.5%
 
-1.71600342510.5%
 
-1.75807558810.5%
 
-1.63409022110.5%
 
Other values (188)18894.9%
 
ValueCountFrequency (%) 
-1.8166310.5%
 
-1.8165610.5%
 
-1.8105310.5%
 
-1.7998310.5%
 
-1.78321032110.5%
 
ValueCountFrequency (%) 
-1.560691910.5%
 
-1.561258810.5%
 
-1.561509910.5%
 
-1.561773810.5%
 
-1.56211910.5%
 

latitude
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct198
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.04383719
Minimum51.98011
Maximum52.07702522
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-14T00:18:41.397492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum51.98011
5-th percentile52.01016617
Q152.0287023
median52.03841892
Q352.06597563
95-th percentile52.07422232
Maximum52.07702522
Range0.09691521539
Interquartile range (IQR)0.0372733309

Descriptive statistics

Standard deviation0.02117500448
Coefficient of variation (CV)0.000406868625
Kurtosis-0.8723273582
Mean52.04383719
Median Absolute Deviation (MAD)0.0138980384
Skewness-0.1004431877
Sum10304.67976
Variance0.0004483808146
MonotocityNot monotonic
2020-10-14T00:18:41.892234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
52.0381621610.5%
 
52.0678319110.5%
 
52.0264924910.5%
 
52.0022132110.5%
 
52.0229101110.5%
 
52.0235537310.5%
 
52.0371057510.5%
 
52.0333461410.5%
 
52.0603433310.5%
 
52.0374896410.5%
 
Other values (188)18894.9%
 
ValueCountFrequency (%) 
51.9801110.5%
 
52.0020113310.5%
 
52.002012810.5%
 
52.0022042710.5%
 
52.0022132110.5%
 
ValueCountFrequency (%) 
52.0770252210.5%
 
52.0765680710.5%
 
52.076365510.5%
 
52.0760242510.5%
 
52.0757770510.5%
 

log10_price
Real number (ℝ)

HIGH CORRELATION

Distinct25
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.811130596
Minimum-2
Maximum3.778151974
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-14T00:18:42.160258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0.004321373783
Q12.000043427
median2.000043427
Q32.176120211
95-th percentile3.191493414
Maximum3.778151974
Range5.778151974
Interquartile range (IQR)0.1760767838

Descriptive statistics

Standard deviation0.9906289171
Coefficient of variation (CV)0.5469671372
Kurtosis2.630265293
Mean1.811130596
Median Absolute Deviation (MAD)0.1760767838
Skewness-1.47733525
Sum358.603858
Variance0.9813456515
MonotocityNot monotonic
2020-10-14T00:18:42.415032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
2.0000434276532.8%
 
0.0043213737833015.2%
 
2.1761202112311.6%
 
2.0792174362211.1%
 
1.778223627105.1%
 
2.36174671894.5%
 
2.20414712573.5%
 
2.3010517152.5%
 
2.41499005152.5%
 
-231.5%
 
Other values (15)199.6%
 
ValueCountFrequency (%) 
-231.5%
 
0.0043213737833015.2%
 
1.778223627105.1%
 
2.0000434276532.8%
 
2.0792174362211.1%
 
ValueCountFrequency (%) 
3.77815197410.5%
 
3.60206107710.5%
 
3.57978473910.5%
 
3.42651288810.5%
 
3.3334492921.0%
 

Interactions

2020-10-14T00:17:38.496414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:39.007929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:39.464317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:39.826386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:40.213284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:40.534154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:40.975466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:41.307369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:41.498602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:41.701171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:41.907486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:42.125968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:42.296843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:42.480930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:42.667092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:42.850566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:43.080047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:43.265190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:43.520619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:43.730702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:43.906403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:44.115083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:44.292875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:44.492141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:44.676375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:44.855645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:45.027094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:45.205214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:45.533031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:45.735988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:45.928241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:46.130266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:46.299982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:46.477602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:46.662201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:46.850500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:47.009661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:47.224431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:47.437654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:47.622553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:47.825226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:48.005396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:48.179730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:48.371202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:48.529486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:48.728950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:48.905041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:49.083728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:49.237855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:49.413597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:49.574519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:49.737359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:49.904311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:50.070714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:50.265841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:50.546490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:50.750488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:50.950601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:51.306716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:51.567652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:51.743917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:51.989375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:52.178315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:52.489380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:52.674088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:52.836509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:53.017920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:53.183629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:53.332410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:53.523368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:53.690540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:53.879976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:54.098741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:54.250282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:54.458088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:54.640786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:54.826217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:55.022497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:55.203911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:55.379334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:55.624348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:55.819385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:56.078977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:56.399907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:56.605123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:56.788632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:56.934384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:57.090937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:57.280690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:57.421079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:57.614887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:57.796472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:57.966606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:58.245055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:58.421491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:58.626953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:58.779397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:59.002547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:59.220343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:59.377590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:59.567630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:17:59.824265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:00.028445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:00.541305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:00.944109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:01.148865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:02.160057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:02.411476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:02.599466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:02.770525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:02.928103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:03.099556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:03.252791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:03.395312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:03.555426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:03.710585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:03.845266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:04.000025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:04.165123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:04.320844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:04.481741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:04.665862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:04.844036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:05.010660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:05.215385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:05.391334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:05.563425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:05.754880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:05.936822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:06.175104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:06.360187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:06.543692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:06.744850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:06.884958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:07.026518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:07.154002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:07.345063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:07.485960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:07.657160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:07.791208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:07.955166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:08.106472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:08.263777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:08.408626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-14T00:18:42.971452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-14T00:18:44.554883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-14T00:18:45.516283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-14T00:18:46.074715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-14T00:18:08.923023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:11.806066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:13.411919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-14T00:18:15.116287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

ObjectIDGlobalIDSelect projectFR1_ProjectNameAsset ID:Asset TypeAsset DescriptionDate InstalledInstalled Cost (£)Watercourse TypeStream Width (m)Land Drainage Consent DifficultyEcological Consent DifficultyAverage member length in Leaky Barrier (m)Wood Diameter (cm)Height of Leaky Barrier above bed (cm)Height of Leaky Barrier above bank (cm)Wood Species UsedOther Wood SpeciesBund height (m)Bund width (m)Bund length (m)Bund MaterialGully Block Length (m)Gully Block Width (m)Gully Block MaterialSoil Equipment or Technique UsedFlood EfficacyFR3 Area UnitsFR3_AreaRoughStorage Created (m3)FR3_AreaIncreasedLossChanged Flood Pathway?Reduced Erosion?Asset ConditionDate AssessedCommentsCreationDateCreatorEditDateEditorlongitudelatitudelog10_price
088333059012-1c4d-43c7-adb7-496972f3c9b4A017ShipstonA017_2006151117leaky_barriers92020-06-02 11:00:001ordinary3.0easyn_a5.020.030.00.0willow_deadNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN135.0NaNnoNaNgood2020-06-15 11:00:00NaN2020-06-15 10:18:01gsmithadmin2020-08-03 11:44:52NaN-1.71507852.0697160.004321
1106602b90a36-d984-4b36-a299-4fbbe884f7b2A017ShipstonA017_2008051630leaky_barriersBLOCKPOTT*12020-08-05 11:00:00260ordinary1.5easyn_a3.0NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN40.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 16:44:24NaN2020-09-18 19:01:54gsmithadmin-1.73287152.0262322.414990
210676f0e9956-8d95-4ebf-a540-9f0cabc735e9A017ShipstonA017_2008051627leaky_barriersBLOCKPOTT*22020-08-05 11:00:00260ordinary1.5easyn_a3.5NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN30.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 16:44:36NaN2020-09-18 19:02:03gsmithadmin-1.73257452.0263122.414990
31068a5d7435c-363e-41bf-ac2e-cea617acbfcdA017ShipstonA017_2008051625leaky_barriersBLOCKPOTT*32020-08-05 11:00:00260ordinary1.5easyn_a3.0NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN25.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 16:44:49NaN2020-09-18 19:02:11gsmithadmin-1.73245052.0263992.414990
4106901f0b340-5a4d-483d-91d0-7161b87d3ccbA017ShipstonA017_2008051622leaky_barriersBLOCKPOTT*42020-08-05 11:00:00260ordinary2.0easyn_a4.0NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN30.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 16:45:02NaN2020-09-18 19:02:50gsmithadmin-1.73244252.0264922.414990
510587b72fa58-62c9-4c1c-9617-a3fb8bf7cd83A017ShipstonA017_2008051617leaky_barriersBLOCKPOTT*52020-08-05 11:00:00260ordinary1.0easyn_a3.0NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhaNaN25.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 15:22:21NaN2020-09-18 19:02:22gsmithadmin-1.73228152.0265922.414990
64421d32eb4f-29cb-4dd9-8e9c-4d94d643dc02A017ShipstonA017_2002241950leaky_barriersBLOCKPOTT01LB12017-10-10 12:00:0060ordinary1.0easyn_a2.510.05.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlowhaNaN10.0NaNnoNaNmoderate2020-02-15 12:00:00Low priority2020-02-24 20:06:31gsmithadmin2020-02-28 07:24:22gsmithadmin-1.73184652.0229101.778224
744306539413-6f5b-4643-8dca-6e8940039a05A017ShipstonA017_2002242006leaky_barriersBLOCKPOTT02LB22017-10-10 12:00:0060ordinary1.0easyn_a2.010.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlowhaNaN10.0NaNNaNNaNmoderate2020-02-15 12:00:00Silted, some scouring. Low Priority2020-02-24 20:13:57gsmithadmin2020-02-28 07:24:32gsmithadmin-1.73191352.0230801.778224
84442e6b604a-fca5-44f1-85e1-118e97dd4afdA017ShipstonA017_2002242017leaky_barriersBLOCKPOTT03LB32017-10-10 12:00:0060ordinary1.5easyn_a2.515.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlowhaNaN10.0NaNnoNaNmoderate2020-02-15 12:00:00Silted. Low priority2020-02-24 20:21:28gsmithadmin2020-02-28 07:24:41gsmithadmin-1.73183852.0232611.778224
94453824078b-cb41-40f2-8e84-469b90340bcbA017ShipstonA017_2002242021leaky_barriersBLOCKPOTT04LB42017-10-10 12:00:0060ordinaryNaNeasyn_aNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlowhaNaN10.0NaNnoNaNmoderate2020-02-15 12:00:00Badly silted. Low priority2020-02-24 20:25:54gsmithadmin2020-02-28 07:24:51gsmithadmin-1.73185752.0234051.778224

Last rows

ObjectIDGlobalIDSelect projectFR1_ProjectNameAsset ID:Asset TypeAsset DescriptionDate InstalledInstalled Cost (£)Watercourse TypeStream Width (m)Land Drainage Consent DifficultyEcological Consent DifficultyAverage member length in Leaky Barrier (m)Wood Diameter (cm)Height of Leaky Barrier above bed (cm)Height of Leaky Barrier above bank (cm)Wood Species UsedOther Wood SpeciesBund height (m)Bund width (m)Bund length (m)Bund MaterialGully Block Length (m)Gully Block Width (m)Gully Block MaterialSoil Equipment or Technique UsedFlood EfficacyFR3 Area UnitsFR3_AreaRoughStorage Created (m3)FR3_AreaIncreasedLossChanged Flood Pathway?Reduced Erosion?Asset ConditionDate AssessedCommentsCreationDateCreatorEditDateEditorlongitudelatitudelog10_price
1886230dcabf5f-35c5-468f-b796-6895259f1549A017ShipstonA017_2004221656leaky_barriersKNEELONG42LB422018-10-22 11:00:00100ordinary1.5easyn_a3.015.030.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN50.0NaNyesNaNgood2020-04-21 23:00:00Some build up behind dam2020-04-22 16:00:44NaN2020-08-15 13:39:31gsmithadmin-1.75638652.0681202.000043
1896194a1f3841-8431-40a0-8da1-2c8b6e2a2f9fA017ShipstonA017_2004211150leaky_barriersKNEELONG43LB432020-04-21 11:00:00100ordinary1.5easyn_a3.015.060.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN50.0NaNnoNaNgood2020-04-21 23:00:00NaN2020-04-21 10:58:28NaN2020-08-15 13:39:42gsmithadmin-1.75645752.0678322.000043
1906182c130aff-9e62-4e69-a852-4b04881bc2b6A017ShipstonA017_2004211152leaky_barriersKNEELONG44LB442018-10-21 11:00:00100ordinary1.0easyn_a2.515.045.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN45.0NaNnoNaNgood2020-04-21 23:00:00Rebar used2020-04-21 10:58:11NaN2020-08-15 13:39:49gsmithadmin-1.75635952.0675832.000043
19161702a1780b-5ee0-4b98-babb-754fa2fadb00A017ShipstonA017_2004211155leaky_barriersKNEELONG45LB452018-10-21 11:00:00100ordinary1.0easyn_a2.515.045.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN80.0NaNnoNaNgood2020-04-21 23:00:00NaN2020-04-21 10:57:57NaN2020-08-15 13:40:00gsmithadmin-1.75637852.0667202.000043
1926299600a2c4-fb34-44a3-809e-99c0101bb269A017ShipstonA017_2004221625leaky_barriersKNEELONG46LB462018-10-22 11:00:00100ordinary1.5easyn_a3.015.060.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN50.0NaNnoNaNgood2020-04-21 23:00:00NaN2020-04-22 16:01:31NaN2020-08-15 13:40:10gsmithadmin-1.75614052.0663472.000043
19362801fb3d79-a96f-401e-92af-c5358ad1ec1fA017ShipstonA017_2004221628leaky_barriersKNEELONG47LB472020-04-22 11:00:00100ordinary1.5easyn_a3.515.060.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN50.0NaNnoNaNgood2020-04-21 23:00:00NaN2020-04-22 16:01:25NaN2020-08-15 13:40:20gsmithadmin-1.75612952.0660762.000043
194627224efb9a-5a7a-4f12-8ebf-3eb368459314A017ShipstonA017_2004221633leaky_barriersKNEELONG48LB482018-10-22 11:00:00100ordinary1.5easyn_a4.015.060.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN60.0NaNnoNaNgood2020-04-21 23:00:00NaN2020-04-22 16:01:20NaN2020-08-15 13:40:28gsmithadmin-1.75612052.0656752.000043
195626ce4a7b74-c608-4cd6-9d9a-fbba4bca2d53A017ShipstonA017_2004221636leaky_barriersKNEELONG49LB492018-10-22 11:00:00100ordinary1.5easyn_a3.515.045.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN45.0NaNnoNaNgood2020-04-21 23:00:00NaN2020-04-22 16:01:14NaN2020-08-15 13:40:37gsmithadmin-1.75594452.0655772.000043
196625fe9b5dee-2d45-4ee1-a16f-d3fb2d967640A017ShipstonA017_2004221639leaky_barriersKNEELONG50LB502020-04-22 11:00:00100ordinary1.5easyn_a3.015.045.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhighhaNaN60.0NaNnoNaNgood2020-04-22 11:00:00Brash to be cleared2020-04-22 16:01:06NaN2020-08-15 13:40:46gsmithadmin-1.75582352.0652322.000043
197624cb9090f8-1682-41b8-ba76-04028f57a6a9A017ShipstonA017_2004221644leaky_barriersKNEELONG51LB512020-04-22 11:00:00100ordinary1.5easyn_a3.015.045.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN70.0NaNnoNaNmoderateNaTMinor repair needed. Banding, re-bedding2020-04-22 16:00:56NaN2020-08-15 13:41:04gsmithadmin-1.75600352.0644002.000043